U.S. patent application number 16/845402 was filed with the patent office on 2020-07-30 for medical image processing apparatus, medical image processing method, and x-ray ct apparatus.
This patent application is currently assigned to CANON MEDICAL SYSTEMS CORPORATION. The applicant listed for this patent is CANON MEDICAL SYSTEMS CORPORATION. Invention is credited to Kazumasa ARAKITA, James BEGELMAN, Victor GORIN, Yiemeng HOI, Joseph MANAK, Jingwu YAO.
Application Number | 20200242770 16/845402 |
Document ID | 20200242770 / US20200242770 |
Family ID | 1000004765483 |
Filed Date | 2020-07-30 |
Patent Application | download [pdf] |
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United States Patent
Application |
20200242770 |
Kind Code |
A1 |
HOI; Yiemeng ; et
al. |
July 30, 2020 |
MEDICAL IMAGE PROCESSING APPARATUS, MEDICAL IMAGE PROCESSING
METHOD, AND X-RAY CT APPARATUS
Abstract
A medical image processing apparatus according to an embodiment
includes processing circuitry. The processing circuitry acquires
image data including image data of a blood vessel of a subject. The
processing circuitry performs analysis related to the blood vessel
by using the image data, and specifies a region of interest in the
blood vessel based on a result of the analysis. The processing
circuitry performs fluid analysis on a region other than the region
of interest at a first accuracy, and performs fluid analysis on the
region of interest at a second accuracy that is higher than the
first accuracy.
Inventors: |
HOI; Yiemeng; (Vernon Hills,
IL) ; MANAK; Joseph; (Vernon Hills, IL) ;
ARAKITA; Kazumasa; (Vernon Hills, IL) ; YAO;
Jingwu; (Vernon Hills, IL) ; BEGELMAN; James;
(Vernon Hills, IL) ; GORIN; Victor; (Vernon Hills,
IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
CANON MEDICAL SYSTEMS CORPORATION |
Otawara-shi |
|
JP |
|
|
Assignee: |
CANON MEDICAL SYSTEMS
CORPORATION
Otawara-shi
JP
|
Family ID: |
1000004765483 |
Appl. No.: |
16/845402 |
Filed: |
April 10, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16411796 |
May 14, 2019 |
10650522 |
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16845402 |
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16032363 |
Jul 11, 2018 |
10346984 |
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16411796 |
|
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15360135 |
Nov 23, 2016 |
10163209 |
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16032363 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T 17/205 20130101;
A61B 6/5217 20130101; G16H 30/40 20180101; G06T 7/0016 20130101;
A61B 6/504 20130101; G06T 2207/30104 20130101; A61B 6/032 20130101;
A61B 6/5205 20130101; G06T 2207/10081 20130101; G06T 2207/20021
20130101 |
International
Class: |
G06T 7/00 20060101
G06T007/00; G16H 30/40 20060101 G16H030/40; A61B 6/03 20060101
A61B006/03; A61B 6/00 20060101 A61B006/00; G06T 17/20 20060101
G06T017/20 |
Claims
1. A medical image processing apparatus comprising processing
circuitry configured to: acquire image data including a coronary
artery region of a subject; acquire distribution of values of a
geometric parameter obtained by analyzing a structure of the
coronary artery region; acquire distribution of index values
related to a pressure of the coronary artery region obtained by
performing fluid analysis on the coronary artery region; and
display the distribution of the values of the geometric parameter
and the distribution of the index values related to the pressure of
the coronary artery region on an image representing the coronary
artery region.
2. The medical image processing apparatus according to claim 1,
wherein the geometric parameter is a parameter calculated based on
a cross-section of a coronary artery at a corresponding
position.
3. The medical image processing apparatus according to claim 1,
wherein the geometric parameter is a parameter related to a lumen
area of a coronary artery at a corresponding position.
4. The medical image processing apparatus according to claim 1,
wherein the geometric parameter is a parameter related to a
stenosis rate of a coronary artery at a corresponding position.
5. The medical image processing apparatus according to claim 1,
wherein the geometric parameter is a parameter indicating whether a
coronary artery bifurcates or becomes stenosed at a corresponding
position.
6. The medical image processing apparatus according to claim 1,
wherein the geometric parameter is a parameter indicating whether
or not calcium is formed at a corresponding position.
7. The medical image processing apparatus according to claim 1,
wherein the geometric parameter is a parameter indicating whether
or not a diameter of a coronary artery at a corresponding position
is smaller than a predetermined size.
8. The medical image processing apparatus according to claim 1,
wherein the processing circuitry is further configured to color the
image representing the coronary artery region in accordance with
the values of the geometric parameter or the index values related
to the pressure of the coronary artery region, and switch, based on
an operation from an operator, between display and non-display of
each of the distribution of the values of the geometric parameter
and the distribution of the index values related to the pressure of
the coronary artery region.
9. The medical image processing apparatus according to claim 1,
wherein the processing circuitry is further configured to set a
region of interest based on the values of the geometric parameter,
set a first analysis model for the region of interest and set a
second analysis model for a region other than the region of
interest, wherein an analysis accuracy of the second analysis model
is lower than that of the first analysis model, generate a
synthesis model by coupling the first analysis model and the second
analysis model, and obtain the index values related to the pressure
of the coronary by performing the fluid analysis using the
synthesis model.
10. The medical image processing apparatus according to claim 1,
wherein the processing circuitry is further configured to color and
display the image representing the coronary artery region in
accordance with the values of the geometric parameter, set a region
of interest on the colored and displayed image in accordance with
an operation from an operator, set a first analysis model for the
region of interest and set a second analysis model for a region
other than the region of interest, wherein an analysis accuracy of
the second analysis model is lower than that of the first analysis
model, generate a synthesis model by coupling the first analysis
model and the second analysis model, and obtain the index values
related to the pressure of the coronary by performing the fluid
analysis using the synthesis model.
11. The medical image processing apparatus according to claim 1,
wherein the processing circuitry is further configured to perform
the fluid analysis on the coronary artery region, set a region of
interest based on a result of the fluid analysis, set a first
analysis model for the region of interest and set a second analysis
model for a region other than the region of interest, wherein an
analysis accuracy of the second analysis model is lower than that
of the first analysis model, generate a synthesis model by coupling
the first analysis model and the second analysis model, and obtain
the index values related to the pressure of the coronary by
performing the fluid analysis using the synthesis model.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 16/411,796, filed May 14, 2019, which is a continuation of U.S.
application Ser. No. 16/032,363, filed Jul. 11, 2018, now U.S. Pat.
No. 10,346,984, which is a continuation of Application of U.S.
application Ser. No. 15/360,135, filed Nov. 23, 2016, now U.S. Pat.
No. 10,163,209, the entire contents of each of which are
incorporated herein by reference.
FIELD
[0002] Embodiments described herein relate generally to a medical
image processing apparatus, a medical image processing method, and
an X-ray CT apparatus.
BACKGROUND
[0003] In the conventionally known technology, fluid analysis on
blood flow has been performed through simulation by using image
data collected by a medical image diagnostic apparatus such as an
X-ray computed tomography (CT) apparatus or a magnetic resonance
imaging (MRI) apparatus.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] FIG. 1 is a diagram illustrating an exemplary configuration
of a medical image processing apparatus according to a first
embodiment;
[0005] FIG. 2 is a flowchart of a processing procedure of fluid
analysis performed by the medical image processing apparatus
according to the first embodiment;
[0006] FIG. 3 is a diagram illustrating an exemplary analysis model
generated by a specification function according to the first
embodiment;
[0007] FIG. 4 is a diagram illustrating an exemplary analysis
result and an exemplary region of interest displayed by the
specification function according to the first embodiment;
[0008] FIG. 5 is a diagram illustrating an exemplary synthesis
model generated by an analysis function according to the first
embodiment;
[0009] FIG. 6 is a diagram illustrating another exemplary analysis
result and another exemplary region of interest displayed by the
specification function according to the first embodiment;
[0010] FIG. 7 is a diagram illustrating an exemplary final result
displayed by the analysis function according to the first
embodiment;
[0011] FIG. 8 is a flowchart of a processing procedure of fluid
analysis performed by a medical image processing apparatus
according to a first modification of the first embodiment;
[0012] FIG. 9 is a diagram illustrating exemplary space
interpolation of an analysis model performed by an analysis
function according to a fourth modification of the first
embodiment;
[0013] FIG. 10 is a diagram illustrating exemplary fluid analysis
performed by an analysis function according to a fifth modification
of the first embodiment;
[0014] FIG. 11 is a diagram illustrating exemplary warning display
related to a branch of a blood vessel performed by a specification
function according to a seventh modification of the first
embodiment; and
[0015] FIG. 12 is a diagram illustrating an exemplary configuration
of an X-ray CT apparatus according to a second embodiment.
DETAILED DESCRIPTION
[0016] A medical image processing apparatus according to an
embodiment includes processing circuitry. The processing circuitry
acquires image data including image data of a blood vessel of a
subject. The processing circuitry performs analysis related to the
blood vessel by using the image data, and specifies a region of
interest in the blood vessel based on a result of the analysis. The
processing circuitry performs fluid analysis on a region other than
the region of interest at a first accuracy, and performs fluid
analysis on the region of interest at a second accuracy that is
higher than the first accuracy.
First Embodiment
[0017] FIG. 1 is a diagram illustrating an exemplary configuration
of a medical image processing apparatus according to a first
embodiment. For example, as illustrated in FIG. 1, this medical
image processing apparatus 100 according to the present embodiment
is mutually connected with an X-ray computed tomography (CT)
apparatus 300 and a medical image storage apparatus 400 through a
network 200 to perform communication therebetween. The medical
image processing apparatus 100 may be additionally connected,
through the network 200, with other medical image diagnostic
apparatuses such as a magnetic resonance imaging (MRI) apparatus,
an X-ray diagnostic apparatus, an ultrasonic diagnostic apparatus,
and a positron emission tomography (PET) apparatus.
[0018] The X-ray CT apparatus 300 collects CT image data related to
the subject. Specifically, the X-ray CT apparatus 300 rotates an
X-ray tube and an X-ray detector around the subject substantially
at a center, and collects projection data by detecting X-ray
transmitted through the subject. Then, the X-ray CT apparatus 300
generates two-dimensional or three-dimensional CT image data based
on the collected projection data.
[0019] The medical image storage apparatus 400 acquires the CT
image data and the projection data from the X-ray CT apparatus 300
through the network 200, and stores the acquired CT image data and
projection data in storage circuitry provided inside or outside of
the apparatus. For example, the medical image storage apparatus 400
is achieved by a computer apparatus such as a server apparatus.
[0020] The medical image processing apparatus 100 acquires the CT
image data from the X-ray CT apparatus 300 or the medical image
storage apparatus 400 through the network 200, and performs various
kinds of image processing on the acquired CT image data. The
medical image processing apparatus 100 is achieved by a computer
apparatus such as a work station.
[0021] For example, the medical image processing apparatus 100
includes interface (I/F) circuitry 110, storage circuitry 120,
input circuitry 130, a display 140, and processing circuitry
150.
[0022] The I/F circuitry 110 is connected with the processing
circuitry 150 and controls various data transmission and
communication performed between the X-ray CT apparatus 300 and the
medical image storage apparatus 400. In the present embodiment, the
I/F circuitry 110 receives the CT image data from the X-ray CT
apparatus 300 or the medical image storage apparatus 400, and
outputs the received CT image data to the processing circuitry 150.
For example, the I/F circuitry 110 is achieved by, for example, a
network card, a network adapter, and a network interface controller
(NIC).
[0023] The storage circuitry 120 is connected with the processing
circuitry 150 and stores therein various kinds of data. In the
present embodiment, the storage circuitry 120 stores therein the CT
image data received from the X-ray CT apparatus 300 or the medical
image storage apparatus 400. For example, the storage circuitry 120
is achieved by a semiconductor memory element such as a random
access memory (RAM) or a flash memory, a hard disk, or an optical
disk.
[0024] The input circuitry 130 is connected with the processing
circuitry 150, converts an input operation received from an
operator into an electric signal, and outputs the electric signal
to the processing circuitry 150. For example, the input circuitry
130 is achieved by a track ball, a switch button, a mouse, a
keyboard, or a touch panel.
[0025] The display 140 is connected with the processing circuitry
150 and displays thereon various kinds of information and various
kinds of image data output from the processing circuitry 150. For
example, the display 140 is achieved by a liquid crystal monitor, a
cathode ray tube (CRT) monitor, or a touch panel.
[0026] The processing circuitry 150 controls each component
included in the medical image processing apparatus 100 in
accordance with an input operation received from the operator
through the input circuitry 130. Specifically, the processing
circuitry 150 stores the CT image data output from the I/F
circuitry 110 in the storage circuitry 120. The processing
circuitry 150 reads the CT image data from the storage circuitry
120 and displays the CT image data on the display 140. For example,
the processing circuitry 150 is achieved by a processor.
[0027] The above description is made on the entire configuration of
the medical image processing apparatus 100 according to the present
embodiment. With such a configuration, the medical image processing
apparatus 100 according to the present embodiment has a function
that performs fluid analysis on blood flow through simulation by
using the three-dimensional CT image data including image data of
the blood vessel of the subject. For example, the medical image
processing apparatus 100 according to the present embodiment is
used in diagnosis or treatment of coronary artery stenosis.
[0028] Typically, the coronary artery stenosis includes a
multifaceted problem, and thus, when reviewing the coronary artery
stenosis, a multitude of information is needed. Simulation of the
blood flow dynamics at the stenosis in fluid analysis can identify
the severity of the stenosis. However, typically, an analysis
result obtained by the simulation performed in fluid analysis tends
to have a lower accuracy when the blood vessel has a complicated
geometric shape or the blood flow exhibits a complicated
pattern.
[0029] For this reason, the medical image processing apparatus 100
according to the present embodiment is configured to perform fluid
analysis on the blood flow at a higher accuracy. The medical image
processing apparatus 100 according to the present embodiment can be
used in a similar manner in a case in which the target is a blood
vessel in other organs such as a brain and a liver, as well as in
the case in which a target of diagnosis or treatment is the
coronary artery.
[0030] Specifically, in the medical image processing apparatus 100
according to the present embodiment, the processing circuitry 150
includes an acquisition function 151, a specification function 152,
and an analysis function 153. The acquisition function 151 is an
exemplary processing circuitry in the claims.
[0031] The acquisition function 151 acquires three-dimensional CT
image data including image data of a blood vessel of the subject.
Specifically, the acquisition function 151 acquires the CT image
data from the X-ray CT apparatus 300 or the medical image storage
apparatus 400, and stores the acquired CT image data in the storage
circuitry 120.
[0032] The specification function 152 performs analysis related to
the blood vessel by using the CT image data, and specifies a region
of interest in the blood vessel of the subject based on a result of
the analysis. Specifically, the specification function 152 reads,
from the storage circuitry 120, the CT image data acquired by the
acquisition function 151, and performs the analysis by using the
read CT image data. Then, the specification function 152 specifies
a region of interest in the blood vessel included in a CT image
based on a result of the analysis.
[0033] The analysis function 153 performs fluid analysis on a
region other than the region of interest at the first accuracy, and
performs fluid analysis on the region of interest at the second
accuracy that is higher than the first accuracy. Specifically, the
analysis function 153 performs, based on the region of interest
specified by the specification function 152, by using the CT image
data acquired by the acquisition function 151, fluid analysis on
the region other than the region of interest at the first accuracy,
and fluid analysis on the region of interest at the second accuracy
that is higher than the first accuracy.
[0034] In this manner, the medical image processing apparatus 100
according to the present embodiment specifies the region of
interest in the blood vessel based on a result of the analysis
related to the blood vessel, and performs fluid analysis on the
specified region of interest at an accuracy that is higher than
that for a region other than the region of interest. Accordingly,
according to the present embodiment, fluid analysis on the blood
flow can be performed at a higher accuracy.
[0035] Each processing function described above is stored in the
storage circuitry 120, for example, as a computer-executable
program. The processing circuitry 150 reads each computer program
from the storage circuitry 120, and executes the read computer
program to achieve the processing function corresponding to the
computer program. In other words, having read the computer
programs, the processing circuitry 150 includes the processing
functions illustrated in FIG. 1.
[0036] Although FIG. 1 illustrates the example in which each
processing function described above is achieved by the single
processing circuitry 150, but the embodiment is not limited
thereto. For example, the processing circuitry 150 may be
configured as a combination of a plurality of independent
processors such that each processor achieves each processing
function by executing the corresponding computer program. The
processing functions included in the processing circuitry 150 may
be achieved through distribution or integration to a single
processing circuitry or a plurality of processing circuitries as
appropriate.
[0037] The following describes processing performed by the medical
image processing apparatus 100 according to the present embodiment
in more detail. The present embodiment describes an example in
which the specification function 152 performs fluid analysis as the
analysis related to the blood vessel.
[0038] FIG. 2 is a flowchart of a processing procedure of fluid
analysis performed by the medical image processing apparatus 100
according to the first embodiment.
[0039] For example, as illustrated in FIG. 2, in the present
embodiment, first, the acquisition function 151 acquires
three-dimensional CT image data including image data of a blood
vessel of the subject from the X-ray CT apparatus 300 or the
medical image storage apparatus 400 (step S101). Specifically,
having received an operation instructing to start fluid analysis
from the operator through the input circuitry 130, the acquisition
function 151 acquires CT image data including image data of the
blood vessel of the subject specified by the operator.
[0040] Subsequently, the specification function 152 executes fluid
analysis by using the CT image data acquired by the acquisition
function 151. Specifically, the specification function 152 performs
fluid analysis on image data of the entire blood vessel included in
the CT image data at a predetermined accuracy. The specification
function 152 performs fluid analysis through simulation using a
finite element method.
[0041] First, the specification function 152 generates an analysis
model of the entire blood vessel included in the CT image data
based on the CT image data at a predetermined accuracy (step
S102).
[0042] FIG. 3 is a diagram illustrating an exemplary analysis model
generated by the specification function 152 according to the first
embodiment. For example, as illustrated in FIG. 3, the
specification function 152 generates an analysis model 31 of the
blood vessel by dividing the shape of the blood vessel included in
the CT image data into a mesh of a plurality of triangles. The
specification function 152 sets the density of the mesh in the
analysis model such that fluid analysis is performed at the
predetermined accuracy described above.
[0043] Thereafter, the specification function 152 executes fluid
analysis by using the generated analysis model (step S103).
Specifically, the specification function 152 calculates a value of
a blood flow parameter as a result of fluid analysis. The blood
flow parameter is, for example, fractional flow reserve (FFR). The
blood flow parameter may be various kinds of parameters other than
the FFR. Examples of the blood flow parameter include blood flow
velocity, velocity gradient, pressure, pressure gradient, pressure
ratio, vorticity, kinetic energy, turbulence intensity, shear
stress, and shear stress gradient.
[0044] Subsequently, the specification function 152 specifies a
region of interest in the blood vessel of the subject based on the
result of fluid analysis (step S104). Specifically, the
specification function 152 specifies the region of interest to be a
region in which the value of the blood flow parameter obtained as
an analysis result through fluid analysis is out of a normal range.
The normal range is determined in advance for each of the various
kinds of parameters of the blood flow described above, and the
range corresponding to the kind of the blood flow parameter used in
analysis is used as appropriate.
[0045] Thereafter, the specification function 152 displays the
result of fluid analysis and the region of interest on the display
140 (step S105). Then, the specification function 152 receives an
operation to change the region of interest, and an operation to
instruct whether to execute analysis on the region of interest from
the operator through the input circuitry 130.
[0046] FIG. 4 is a diagram illustrating an exemplary analysis
result and an exemplary region of interest displayed by the
specification function 152 according to the first embodiment. For
example, as illustrated in FIG. 4, the specification function 152
displays, on the display 140, a graphic 41 illustrating the shape
of an analysis model. Then, the specification function 152
displays, on the graphic 41 illustrating an analysis model,
information indicating distribution of the value of the blood flow
parameter obtained by fluid analysis. For example, the
specification function 152 allocates different colors to values of
the blood flow parameter, and colors the graphic 41 illustrating an
analysis model in accordance with spatial distribution of the
values of the blood flow parameter in the region of interest. The
specification function 152 displays, on the graphic 41 illustrating
an analysis model, a graphic 42 illustrating a specified range of
the region of interest. When having received an operation to change
the region of interest, the specification function 152 changes the
range of the region of interest in accordance with the received
operation, and displays, on the graphic 41 illustrating an analysis
model, the graphic 42 illustrating the changed region of
interest.
[0047] Then, if the specification function 152 has received an
operation instructing execution of analysis on the region of
interest (Yes at step S106), the analysis function 153 executes
fluid analysis based on the region of interest being set at the
time. Similarly to the specification function 152, the analysis
function 153 performs fluid analysis through simulation using the
finite element method.
[0048] In the first fluid analysis, the analysis function 153
performs fluid analysis on a region other than the region of
interest at a predetermined accuracy same as that of fluid analysis
performed by the specification function 152, and performs fluid
analysis on the region of interest at an accuracy that is higher
than the predetermined accuracy.
[0049] The analysis function 153 performs fluid analysis at an
accuracy that is higher than the predetermined accuracy by
increasing the resolution of image data as a basis of an analysis
model used in fluid analysis. The analysis function 153 performs
fluid analysis at an accuracy that is higher than the predetermined
accuracy by increasing a calculation cost of fluid analysis. For
example, the analysis function 153 increases the calculation cost
of fluid analysis by increasing the density of the mesh in an
analysis model by the finite element method.
[0050] Specifically, the analysis function 153 requests the X-ray
CT apparatus 300 to reconstruct enlarged three-dimensional CT image
data of a range in which the region of interest is being set at the
time, and acquires the enlarged and reconstructed CT image data of
the region of interest from the X-ray CT apparatus 300 (step S107).
Accordingly, the CT image data of the region of interest having an
increased resolution is obtained.
[0051] Subsequently, the analysis function 153 generates, based on
the enlarged and reconstructed CT image data of the region of
interest, an analysis model of the region of interest having an
increased mesh density as compared with that in the analysis model
used in the fluid analysis performed by the specification function
152 (step S108).
[0052] Instead of using the enlarged and reconstructed CT image
data of the region of interest, the analysis function 153 may
generate an analysis model of the region of interest having an
increased mesh density obtained by setting a mesh finer than the
mesh of the analysis model used in the fluid analysis performed by
the specification function 152. This configuration eliminates the
need to acquire the enlarged and reconstructed CT image data of the
region of interest. In addition, with this configuration, after
having generated an analysis model having a mesh with an increased
density, the analysis function 153 may correct the mesh by
comparing the generated analysis model with the CT image data.
[0053] Thereafter, the analysis function 153 generates a synthesis
model obtained by coupling part of the analysis model of the entire
blood vessel generated at the predetermined accuracy described
above and corresponding to a part other than the region of
interest, and the analysis model of the region of interest
generated at an accuracy that is higher than the predetermined
accuracy (step S109).
[0054] FIG. 5 is a diagram illustrating an exemplary synthesis
model generated by the analysis function 153 according to the first
embodiment. For example, as illustrated in FIG. 5, the analysis
function 153 generates a synthesis model obtained by coupling parts
31a, 31b, and 31c of the analysis model 31 of the entire blood
vessel, corresponding parts other than the region of interest, and
an analysis model 51 of the region of interest. When such a
synthesis model is used, fluid analysis is performed on the region
of interest at an accuracy that is higher than that for a part
other than the region of interest.
[0055] Then, the analysis function 153 executes fluid analysis on
the entire blood vessel by using the generated synthesis model
(step S110).
[0056] Thereafter, the specification function 152 specifies the
region of interest again based on an analysis result obtained by
newly performed fluid analysis (step S104), and displays a result
of the fluid analysis and the region of interest on the display 140
(step S105).
[0057] FIG. 6 is a diagram illustrating another exemplary analysis
result and another exemplary of the region of interest displayed by
the specification function 152 according to the first embodiment.
For example, similarly to the example illustrated in FIG. 4, as
illustrated in FIG. 6, the specification function 152 displays the
graphic 41 illustrating the shape of an analysis model, information
indicating distribution of values of the blood flow parameter
obtained by fluid analysis, and the graphic 42 illustrating a
region of interest. The graphic 41 illustrating the shape of the
analysis model is displayed based on a synthesis model used in the
newly performed fluid analysis. The information indicating the
distribution of values of the blood flow parameter is displayed
based on the analysis result obtained by the newly performed fluid
analysis. The graphic 42 illustrating the region of interest is
displayed based on a previously set region of interest.
[0058] The operator can determine whether to execute fluid analysis
again by referring to the result of fluid analysis displayed on the
display 140. Then, if the specification function 152 has again
received an operation instructing execution of analysis on the
region of interest (Yes at step S106), the analysis function 153
executes fluid analysis again based on the region of interest being
set at the time.
[0059] In the second and following fluid analysis, the analysis
function 153 performs fluid analysis on the region other than the
region of interest at an accuracy same as that in the previously
performed fluid analysis, and performs fluid analysis on the region
of interest at an accuracy that is higher than that in the
previously performed fluid analysis.
[0060] In this case, the analysis function 153 performs fluid
analysis at an accuracy that is higher than that of the previously
performed fluid analysis by increasing the resolution of image data
as a basis of an analysis model used in fluid analysis. The
analysis function 153 also performs fluid analysis at an accuracy
that is higher than that in the previously performed fluid analysis
by increasing a calculation cost of fluid analysis. For example,
the analysis function 153 increases the calculation cost of fluid
analysis by increasing the density of a mesh in an analysis model
by the finite element method.
[0061] Specifically, similarly to the first fluid analysis, the
analysis function 153 acquires the enlarged and reconstructed CT
image data of the region of interest from the X-ray CT apparatus
300 (step S107). Then, the analysis function 153 generates an
analysis model of the region of interest having an increased mesh
density as compared with that of an analysis model used in the
previously performed fluid analysis by using the acquired CT image
data (step S108).
[0062] Thereafter, the analysis function 153 generates a synthesis
model obtained by coupling part of the analysis model of the entire
blood vessel generated for the previously performed fluid analysis
and corresponding to a part other than the region of interest, and
the analysis model of the region of interest generated for new
fluid analysis (step S109). Then, the analysis function 153
executes fluid analysis on the entire blood vessel by using the
generated synthesis model (step S110).
[0063] In this manner, while the specification function 152 is
receiving an operation instructing execution of analysis on the
region of interest (Yes at step S106), the analysis function 153
repeats the execution of fluid analysis. Then, if the specification
function 152 receives an operation instructing no execution of
analysis on the region of interest (step S106, No), the analysis
function 153 displays a result of previously performed fluid
analysis as a final result on the display 140 (step S111).
[0064] FIG. 7 is a diagram illustrating an exemplary final result
displayed by the analysis function 153 according to the first
embodiment. For example, as illustrated in FIG. 7, similarly to the
examples illustrated in FIGS. 4 and 6, the analysis function 153
displays, as the final result, the graphic 41 illustrating the
shape of an analysis model, information indicating distribution of
values of the blood flow parameter obtained by fluid analysis, and
the graphic 42 illustrating the region of interest. The graphic 41
illustrating the shape of an analysis model is displayed based on a
synthesis model used in the previously performed fluid analysis.
The information indicating the distribution of values of the blood
flow parameter is displayed based on a result of the previously
performed fluid analysis. The graphic 42 illustrating the region of
interest is displayed based on the previously set region of
interest.
[0065] In addition, for example, the analysis function 153
displays, as the final result, information indicating the value of
a vascular geometric parameter related to an analysis model of the
blood vessel. Examples of the vascular geometric parameters
include, a blood vessel centerline, a coronary vessel lumen area, a
coronary vessel diameter (size), a minimal luminal area, a minimal
luminal diameter (size), the ratio of the minimal luminal area to
the coronary vessel area, the ratio of the minimal luminal diameter
to the coronary vessel size, a change of the coronary vessel lumen
area along the vessel length, and the percentage of the coronary
vessel lumen area to a pre-defined referenced area.
[0066] For example, similarly to the blood flow parameter, the
specification function 152 allocates different colors to values of
the vascular geometric parameter, and colors the graphic 41
illustrating an analysis model in accordance with spatial
distribution of the values of the vascular geometric parameter in
the region of interest.
[0067] For example, the specification function 152 receives, from
the operator through the input circuitry 130, an operation to
select display or non-display for each of information related to
the blood flow parameter and information related to vascular
geometric parameter. Then, the specification function 152 displays
the information selected by the operator in accordance with the
received operation on the graphic 41 illustrating an analysis
model. For example, as illustrated in FIG. 7, the specification
function 152 displays, on the display 140, a check box for
selecting display or non-display for each of the blood flow
parameter and vascular geometric parameter, and receives an
operation to select display or non-display of each information from
the operator.
[0068] Each step described above is achieved by, for example, the
processing circuitry 150 loading the pre-defined computer program
corresponding to each function from the storage circuitry 120 and
executing the pre-defined computer program. Specifically, step S101
is achieved by, for example, the processing circuitry 150 loading
the pre-defined computer program corresponding to the acquisition
function 151 from the storage circuitry 120 and executing the
pre-defined computer program. Steps S102 to S106 are achieved by,
for example, the processing circuitry 150 loading the pre-defined
computer program corresponding to the specification function 152
from the storage circuitry 120 and executing the pre-defined
computer program. Steps S107 to S111 are achieved by, for example,
the processing circuitry 150 loading the pre-defined computer
program corresponding to the analysis function 153 from the storage
circuitry 120 and executing the pre-defined computer program.
[0069] As described above, in the first embodiment, the medical
image processing apparatus 100 specifies a region of interest in a
blood vessel based on a result of fluid analysis, and performs
fluid analysis on the specified region of interest at an accuracy
that is higher than that in a region other than the region of
interest. Accordingly, according to the present embodiment, fluid
analysis on the blood flow can be performed at a higher
accuracy.
[0070] The first embodiment described above may be modified as
appropriate. The following describes a plurality of modifications
of the first embodiment. The configuration of a medical image
processing apparatus according to each modification is basically
the same as the configuration illustrated in FIG. 1, and thus the
following description is focused on any difference from the
description in the first embodiment.
First Modification of First Embodiment
[0071] For example, the first embodiment above describes the
example in which the specification function 152 performs fluid
analysis as the analysis related to a blood vessel and specifies a
region of interest in the blood vessel of the subject based on a
result of this fluid analysis, but the present embodiment is not
limited thereto. For example, the specification function 152 may
perform image analysis as the analysis related to a blood vessel
and specify a region of interest based on a result of this image
analysis.
[0072] FIG. 8 is a flowchart of processing procedure of fluid
analysis performed by the medical image processing apparatus 100
according to a first modification of the first embodiment.
[0073] For example, as illustrated in FIG. 8, in the present
modification, similarly to the first embodiment, the acquisition
function 151 first acquires three-dimensional CT image data
including image data of a blood vessel of the subject from the
X-ray CT apparatus 300 or the medical image storage apparatus 400
(step S201).
[0074] Subsequently, the specification function 152 executes image
analysis using the CT image data acquired by the acquisition
function 151 (step S202). The specification function 152 specifies
an abnormal region in the blood vessel by performing image analysis
based on a pixel value included in the CT image data. Examples of
the abnormal region specified by the specification function 152
include a region in which the amount of lesional tissue such as
calcium exceeds a pre-defined amount, a region in which an artifact
is present, and a region in which the blood vessel has a diameter
smaller than a pre-defined threshold.
[0075] Subsequently, the specification function 152 specifies a
region of interest in the blood vessel of the subject based on a
result of the image analysis (step S203). For example, the
specification function 152 specifies the region of interest to be
the abnormal region obtained by the image analysis.
[0076] Thereafter, the specification function 152 displays the
result of the image analysis and the region of interest on the
display 140 (step S204). Similarly to the first embodiment, the
specification function 152 receives, from the operator through the
input circuitry 130, an operation to change the region of interest,
and an operation to instruct whether to execute analysis on the
region of interest.
[0077] For example, similarly to the example illustrated in FIG. 4,
the specification function 152 displays, on the display 140, the
graphic 41 illustrating the shape of an analysis model. Then, the
specification function 152 displays, on the graphic 41 illustrating
the analysis model, information indicating a region determined to
be a lesion by the image analysis, and information indicating a
region in which an artifact is determined to be present. For
example, the specification function 152 colors, on the graphic 41
illustrating the analysis model, the region determined to be a
lesion by the image analysis, and the region in which an artifact
is determined to be present. The specification function 152 also
displays, on the graphic 41 illustrating the analysis model, the
graphic 42 illustrating the range of the specified region of
interest. When having received an operation to change the region of
interest, the specification function 152 changes the range of the
region of interest in accordance with the received operation, and
displays, on the graphic 41 illustrating the analysis model, the
graphic 42 illustrating the changed region of interest.
[0078] Then, if the specification function 152 has received an
operation instructing execution of analysis on the region of
interest (Yes at step S205), the analysis function 153 executes
fluid analysis based on the region of interest being set at the
time. Similarly to the first embodiment, the analysis function 153
performs the fluid analysis through simulation using the finite
element method.
[0079] In the first fluid analysis, the analysis function 153
performs fluid analysis on a region other than the region of
interest at a pre-defined accuracy, and performs fluid analysis on
the region of interest at an accuracy that is higher than the
pre-defined accuracy.
[0080] In this case, the analysis function 153 performs fluid
analysis at an accuracy that is higher than the pre-defined
accuracy by increasing the resolution of image data as a basis of
an analysis model used in fluid analysis. The analysis function 153
also performs fluid analysis at an accuracy that is higher than the
pre-defined accuracy by increasing the calculation cost of fluid
analysis. For example, the analysis function 153 increases the
calculation cost of fluid analysis by increasing the density of a
mesh in an analysis model by the finite element method.
[0081] Specifically, similarly to the first embodiment, the
analysis function 153 requests the X-ray CT apparatus 300 to
reconstruct enlarged three-dimensional CT image data of a range in
which the region of interest is being set at the time, and acquires
the enlarged and reconstructed CT image data of the region of
interest from the X-ray CT apparatus 300 (step S206).
[0082] Subsequently, the analysis function 153 generates, based on
the CT image data used in the image analysis performed by the
specification function 152, an analysis model of a region other
than the region of interest in the blood vessel included in the CT
image data at the pre-defined accuracy described above (step S207).
In addition, the analysis function 153 generates, based on the
enlarged and reconstructed CT image data of the region of interest,
an analysis model of the region of interest having an increased
mesh density as compared with that of the analysis model generated
at the pre-defined accuracy described above (step S208).
[0083] Thereafter, the analysis function 153 generates a synthesis
model obtained by coupling the analysis model of the region other
than the region of interest generated at the pre-defined accuracy
described above, and the analysis model of the region of interest
generated at an accuracy that is higher than the pre-defined
accuracy (step S209). When such a synthesis model is used,
similarly to the first embodiment, fluid analysis is performed on
the region of interest at an accuracy that is higher than that for
a part other than the region of interest.
[0084] Then, the analysis function 153 executes fluid analysis on
the entire blood vessel by using the generated synthesis model
(step S210).
[0085] Thereafter, the specification function 152 specifies the
region of interest again based on an analysis result obtained by
the newly performed fluid analysis (step S203), and displays a
result of fluid analysis and the region of interest on the display
140 (step S204).
[0086] Similarly to the first embodiment, the operator can
determine whether to execute fluid analysis again by referring to
the result of fluid analysis displayed on the display 140. Then, if
the specification function 152 has again received an operation
instructing execution of analysis on the region of interest (Yes at
step S205), the analysis function 153 executes fluid analysis again
based on the region of interest being set at the time.
[0087] In the second and the following fluid analysis, the analysis
function 153 performs fluid analysis on the region other than the
region of interest at an accuracy same as that in the previously
performed fluid analysis, and performs fluid analysis on the region
of interest at an accuracy that is higher than that in the
previously performed fluid analysis.
[0088] In this case, the analysis function 153 performs fluid
analysis at an accuracy that is higher than that of the previously
performed fluid analysis by increasing the resolution of image data
as a basis of an analysis model used in fluid analysis. The
analysis function 153 also performs fluid analysis at an accuracy
that is higher than that of the previously performed fluid analysis
by increasing the calculation cost of fluid analysis. For example,
the analysis function 153 increases the calculation cost of fluid
analysis by increasing the density of a mesh in an analysis model
by the finite element method.
[0089] Specifically, similarly to the first fluid analysis, the
analysis function 153 acquires the enlarged and reconstructed CT
image data of the region of interest from the X-ray CT apparatus
300 (step S206). Then, the analysis function 153 generates an
analysis model of fluid analysis at the pre-defined accuracy
described above for the region other than the region of interest in
the blood vessel by using the CT image data used in the image
analysis performed by the specification function 152 (step S207).
The analysis function 153 also generates, by using the acquired CT
image data, an analysis model of the region of interest having an
increased mesh density as compared with that of the analysis model
used in the previously performed fluid analysis (step S208).
[0090] Thereafter, the analysis function 153 generates a synthesis
model obtained by coupling the analysis model of the region other
than the region of interest generated at the pre-defined accuracy
described above, and the analysis model of the region of interest
generated at an accuracy that is higher than the pre-defined
accuracy (step S209). Then, the analysis function 153 executes
fluid analysis on the entire blood vessel by using the generated
synthesis model (step S210).
[0091] In this manner, while the specification function 152 is
receiving an operation instructing execution of analysis on the
region of interest (Yes at step S205), the analysis function 153
repeats the execution of fluid analysis. Then, if the specification
function 152 receives an operation instructing no execution of
analysis on the region of interest (No at step S205), the analysis
function 153 displays a result of the previously performed fluid
analysis as a final result on the display 140 (step S211). For
example, similarly to the example illustrated in FIG. 7, the
analysis function 153 displays the final result.
[0092] Each step described above is achieved by, for example, the
processing circuitry 150 loading the pre-defined computer program
corresponding to each function from the storage circuitry 120 and
executing the pre-defined computer program. Specifically, step S201
is achieved by, for example, the processing circuitry 150 loading
the pre-defined computer program corresponding to the acquisition
function 151 from the storage circuitry 120 and executing the
pre-defined computer program. Steps S202 to S205 are achieved by,
for example, the processing circuitry 150 loading the pre-defined
computer program corresponding to the specification function 152
from the storage circuitry 120 and executing the pre-defined
computer program. Steps S206 to S211 are achieved by, for example,
the processing circuitry 150 loading the pre-defined computer
program corresponding to the analysis function 153 from the storage
circuitry 120 and executing the pre-defined computer program.
[0093] As described above, in the first modification of the first
embodiment, the medical image processing apparatus 100 specifies a
region of interest in a blood vessel based on a result of image
analysis, and performs fluid analysis on the specified region of
interest at an accuracy that is higher than that in a region other
than the region of interest. Accordingly, according to the present
modification, fluid analysis on the blood flow can be performed at
a higher accuracy.
Second Modification of First Embodiment
[0094] The first modification above describes the example in which
the specification function 152 automatically performs image
analysis, but the present embodiment is not limited thereto. For
example, the specification function 152 may include a function to
perform blood vessel analysis in accordance with an operation
received from the operator.
[0095] In the present modification, the specification function 152
performs, as the analysis related to a blood vessel, blood vessel
analysis that analyzes the structure of the blood vessel in
accordance with an operation received from the operator, and
specifies a region of interest to be an abnormal region obtained as
a result of the blood vessel analysis.
[0096] Specifically, the specification function 152 extracts
information such as a blood vessel centerline, a blood vessel wall,
and a bifurcated shape from the blood vessel visualized in the CT
image data in accordance with the operation received from the
operator, and identifies, for example, a region in which a stenosis
is generated and a stenosis rate based on the extracted
information.
[0097] For example, the specification function 152 identifies the
stenosis rate of the blood vessel in accordance with an operation
received from the operator, and specifies a region of interest to
be a range in which the stenosis rate exceeds a pre-defined
threshold. For example, the specification function 152 specifies
the region of interest to be a region having a stenosis rate of 50%
or larger.
[0098] Then, in the present modification, the analysis function 153
may start fluid analysis while the specification function 152 is
performing specification of the region of interest. In this case,
when the region of interest is specified by the specification
function 152, the analysis function 153 uses a result of fluid
analysis completed so far for a region other than the region of
interest. Accordingly, a time taken for fluid analysis on the blood
vessel can be reduced.
Third Modification of First Embodiment
[0099] The first embodiment and the modifications above each
describe the example in which the analysis function 153 increases
the calculation cost of fluid analysis by increasing the density of
a mesh in an analysis model by the finite element method, but the
present embodiment is not limited thereto.
[0100] For example, the analysis function 153 may increase the
calculation cost of fluid analysis through change from
one-dimensional analysis to three-dimensional analysis.
Alternatively, the analysis function 153 may increase the
calculation cost of fluid analysis by increasing the convergence
number of iterative calculation performed in fluid analysis.
Fourth Modification of First Embodiment
[0101] In the first embodiment and the modifications described
above, the analysis function 153 generates a synthesis model by
coupling an analysis model related to a region other than the
region of interest and an analysis model related to the region of
interest, but the analysis function 153 may smoothly couple the
analysis models by applying space interpolation where the analysis
models are coupled with each other at the generation of a synthesis
model.
[0102] FIG. 9 is a diagram illustrating exemplary space
interpolation of an analysis model performed by the analysis
function 153 according to a fourth modification of the first
embodiment. A plurality of points illustrated in FIG. 9 represent
contact points of a mesh in an analysis model by the finite element
method. Four contact points 91a to 91d illustrated in an upper side
in FIG. 9 are contact points set in an analysis model related to
the region other than the region of interest. Five contact points
91e to 91i illustrated in a lower side in FIG. 9 are contact points
set in an analysis model related to the region of interest.
[0103] For example, as illustrated in FIG. 9, in the analysis model
related to the region of interest, a finer mesh is set than that in
the analysis model of the region other than the region of interest,
and thus the contact points have shorter intervals therebetween.
Thus, when a synthesis model is generated, the positions of contact
points are shifted from each other where the analysis models are
coupled due to, for example, a partial volume effect in some cases.
For example, as illustrated in FIG. 9, a difference occurs between
contact point 91d positioned in a coupled-side end part of the
analysis model related to the region other than the region of
interest, and contact point 91e positioned in a coupled-side end
part of the analysis model related to the region of interest.
[0104] In such a case, the analysis function 153 smoothly couples
the analysis models by applying space interpolation by using a
method such as spline interpolation where the analysis models are
coupled. For example, as illustrated in FIG. 9, the analysis
function 153 interpolates, in place of contact point 91d positioned
in the coupled-side end part of the analysis model related to the
region other than the region of interest, new contact point 91j
based on the positions of other contact points. In this manner, the
analysis model related to the region of interest and the analysis
model of the region other than the region of interest can be
smoothly coupled, thereby achieving an improved accuracy of fluid
analysis using a synthesis model.
Fifth Modification of First Embodiment
[0105] In the first embodiment and the modifications described
above, the analysis function 153 performs fluid analysis using a
synthesis model, but an analysis model used in the previously
performed fluid analysis is used for a region upstream of the
region of interest in the synthesis model, and thus fluid analysis
provides a result same as the previously obtained result.
[0106] Thus, when performing fluid analysis using a synthesis
model, the analysis function 153 may omit calculation in fluid
analysis using an analysis result obtained by the previously
performed fluid analysis for a region positioned upstream of the
region of interest among regions other than the region of
interest.
[0107] FIG. 10 is a diagram illustrating exemplary fluid analysis
performed by the analysis function 153 according to a fifth
modification of the first embodiment. Diagrams illustrated on the
left and right sides in FIG. 10 illustrate a synthesis model 101 of
the entire blood vessel, and arrows with dashed lines illustrate
the flow direction of the blood flow. In the diagram illustrated on
the right side in FIG. 10, each range illustrated with a
double-headed arrow illustrates the range of a region of interest R
in the synthesis model 101.
[0108] For example, in the example illustrated in FIG. 10, the
analysis function 153 omits calculation in fluid analysis using an
analysis result obtained by the previously performed fluid analysis
for a region Ru positioned upstream of the region of interest R
among regions other than the region of interest R. The analysis
function 153 also newly performs fluid analysis using the synthesis
model 101 on the region of interest R and a region Rd positioned
downstream of the region of interest R among regions other than the
region of interest. Accordingly, a time taken for fluid analysis on
the blood vessel can be reduced.
Sixth Modification of First Embodiment
[0109] In the first embodiment and the modifications described
above, the specification function 152 displays a result of analysis
related to a blood vessel on the display 140, but the specification
function 152 may perform both of fluid analysis and image analysis
and additionally display, based on a result of the image analysis,
the reliability of an analysis result obtained by the fluid
analysis.
[0110] Specifically, similarly to the first embodiment, the
specification function 152 performs fluid analysis using the CT
image data acquired by the acquisition function 151. Similarly to
the first modification, the specification function 152 performs
image analysis using the CT image data acquired by the acquisition
function 151, and specifies an abnormal region in the blood vessel.
For example, the specification function 152 specifies the abnormal
region to be, for example, a region in which the amount of lesional
tissue such as calcium exceeds a pre-defined amount, a region in
which an artifact such as a motion artifact or a banding artifact
is present, or a region in which the blood vessel has a diameter
smaller than a pre-defined threshold.
[0111] Then, similarly to the first embodiment, the specification
function 152 displays a result of fluid analysis and the region of
interest on the display 140. In addition, the specification
function 152 calculates the reliability of fluid analysis at each
position in the blood vessel based on an analysis value obtained as
a result of image analysis, and displays the calculated reliability
together with the result of fluid analysis on the display 140.
[0112] For example, the specification function 152 calculates the
reliability of fluid analysis at each position in the blood vessel
based on the amount of lesional tissue such as calcium, the number
of artifacts, and the coronary vessel diameter. Then, for example,
as illustrated in FIG. 4, the specification function 152 displays,
on the graphic 41 illustrating the shape of an analysis model,
information indicating distribution of values of the blood flow
parameter, and additionally displays information indicating the
calculated reliability on the graphic 41. For example, the
specification function 152 allocates different colors to values of
the reliability, and colors the graphic 41 illustrating an analysis
model in accordance with spatial distribution of the reliability in
the blood vessel. Alternatively, the specification function 152 may
display, on the graphic 41 illustrating an analysis model or near
the graphic 41, a numerical value indicating the reliability.
Accordingly, the operator can be presented with information to be
referred to when determining whether to execute fluid analysis
again.
Seventh Modification of First Embodiment
[0113] The specification function 152 may give notification of
warning when the blood vessel includes a branch having a thickness
larger than a pre-defined threshold and the reliability is lower
than a pre-defined threshold in the region of the branch.
[0114] FIG. 11 is a diagram illustrating exemplary warning display
related to a branch of the blood vessel performed by the
specification function 152 according to a seventh modification of
the first embodiment. For example, as illustrated in FIG. 11, the
specification function 152 detects a branch 112 that is a blood
vessel bifurcating from a blood vessel 111 based on the CT image
data acquired by the acquisition function 151, and calculates a
coronary vessel diameter D of the branch 112 where the detected
branch 112 and the blood vessel 111 connects with each other. Then,
for example, when the calculated coronary vessel diameter D of
branch 112 is 3 mm or larger, the acquisition function 151 detects
whether there exists a region that includes a connected part
between the blood vessel 111 and the branch 112 and in which the
reliability of fluid analysis is lower than a pre-defined
threshold. Then, if there exists such a region Rb, the acquisition
function 151 displays, on the display 140, information indicating
the region Rb and a warning message together with a result of fluid
analysis.
[0115] Typically, in fluid analysis through simulation, when the
blood vessel is finer or the blood vessel bifurcates, an analysis
result has a lower accuracy in some cases. In other words, when the
blood vessel is thick enough but the analysis result has a low
reliability, it is likely that accurate fluid analysis has not been
performed. In such a case, in the present modification, the
operator can be prompted to perform fluid analysis again.
Eighth Modification of First Embodiment
[0116] The first embodiment and the modifications above describe
the example in which an analysis model of the region of interest
having an increased mesh density is generated based on the enlarged
and reconstructed CT image data of the region of interest, but the
present embodiment is not limited thereto. For example, instead of
acquiring the enlarged and reconstructed CT image data of the
region of interest, the analysis function 153 may perform image
correction on the CT image data, and then perform fluid analysis at
an improved accuracy by using the corrected image data.
[0117] In this case, instead of generating an analysis model of the
region of interest having an increased mesh density based on the
enlarged and reconstructed CT image data of the region of interest,
the analysis function 153 performs image correction on the CT image
data, and then generates, based on the corrected image data, an
analysis model of the region of interest having an increased mesh
density. For example, the analysis function 153 performs, on the CT
image data, pre-defined image corrections such as motion artifact
correction, banding artifact correction, and phase correction.
Ninth Modification of First Embodiment
[0118] Instead of performing the pre-defined image corrections as
the image correction described above, the analysis function 153 may
perform an image correction selected from among a plurality of
kinds of image corrections. In this case, for example, the content
of each image correction is stored in the storage circuitry 120 as
a database in advance. Examples of the content of image correction
include correction of a blood vessel wall, correction of a fake
stenosis, and smoothing of pixel value change.
[0119] Then, for example, the analysis function 153 performs an
image correction selected from the database by the operator.
Alternatively, the analysis function 153 may automatically select
an appropriate image correction from the database in accordance
with results of image analysis and fluid analysis in accordance
with a pattern associating an analysis result of the CT image data
and the kind of an image correction in advance, and perform the
selected image correction. Accordingly, the accuracy of fluid
analysis can be increased.
Tenth Modification of First Embodiment
[0120] The first embodiment and the modifications described above
describe the example in which the acquisition function 151 acquires
the CT image data from the X-ray CT apparatus 300 or the medical
image storage apparatus 400, but the present embodiment is not
limited thereto. For example, instead of acquiring the CT image
data, the acquisition function 151 may acquire projection data as a
basis of the CT image data.
[0121] In this case, the acquisition function 151 acquires, from
the X-ray CT apparatus 300 or the medical image storage apparatus
400, projection data as a basis of three-dimensional CT image data
including image data of a blood vessel of the subject, and stores
the acquired projection data in the storage circuitry 120. Then,
when performing analysis related to a blood vessel, the
specification function 152 and the analysis function 153 read the
projection data from the storage circuitry 120, and reconstruct, or
reconstruct in an enlarged manner, CT image data used in analysis
from the read projection data.
[0122] Alternatively, similarly to the first embodiment, the
acquisition function 151 may acquire CT image data, and the
specification function 152 and the analysis function 153 may read
the CT image data from the storage circuitry 120 and use the read
CT image data to generate CT image data used in analysis. For
example, when analysis related to a blood vessel is performed, the
CT image data is produced back to projection data by a method such
as a forward projection, and CT image data used in analysis is
reconstructed, or reconstructed in an enlarged manner, from the
projection data thus obtained.
Eleventh Modification of First Embodiment
[0123] The first embodiment and the modifications described above
describe the example in which the medical image processing
apparatus 100 displays a result of fluid analysis on the display
140 included in the apparatus, but the present embodiment is not
limited thereto. For example, the medical image processing
apparatus 100 may output an analysis result to a medical image
display apparatus connected through the network 200.
[0124] Recently, a medical image processing system has been
configured in some case as a thin client system in which a client
apparatus used by the operator executes minimum necessary
processing, and a server apparatus executes a large part of
processing. For example, in such a medical image processing system,
a processing circuitry included in the server apparatus may have a
configuration similarly to that of the medical image processing
apparatus 100 described in the first embodiment and the
modifications described above, and the client apparatus (medical
image display apparatus) may acquire, from the medical image
processing apparatus 100, a result of analysis performed by the
medical image processing apparatus 100, and display the result. For
example, the client apparatus displays the analysis result through,
for example, a general-purpose browser installed in the apparatus
in advance.
Twelfth Modification of First Embodiment
[0125] The first embodiment and the modifications described above
describe the example in which the CT image data collected by the
X-ray CT apparatus 300 is used as image data related to the
subject, but the present embodiment is not limited thereto. For
example, image data collected by another medical image diagnostic
apparatus may be used as the image data related to the subject.
Examples of the medical image diagnostic apparatus include an MRI
apparatus, an X-ray diagnostic apparatus, an ultrasonic diagnostic
apparatus, and a PET apparatus.
Second Embodiment
[0126] The first embodiment and the modifications described above
describe the example in which the medical image processing
apparatus 100 executes various kinds of processing, but the present
embodiment is not limited thereto. For example, the X-ray CT
apparatus 300 may execute the various kinds of processing described
above. The following describes this example as the second
embodiment.
[0127] FIG. 12 is a diagram illustrating an exemplary configuration
of the X-ray CT apparatus 300 according to the second embodiment.
For example, as illustrated in FIG. 1, the X-ray CT apparatus 300
according to the present embodiment includes a gantry 310, a couch
320, and a console 330.
[0128] The gantry 310 is an apparatus configured to irradiate the
subject S (patient) with X-ray, detect X-ray transmitted through
the subject S, and output the detected X-ray to the console 330.
For example, the gantry 310 includes an X-ray generation device
311, an X-ray irradiation control circuitry 312, a detector 313, a
rotational frame 314, a gantry drive circuitry 315, and a data
collection circuitry 316.
[0129] The X-ray generation device 311 generates X-ray and
irradiates the subject S with the generated X-ray. For example, the
X-ray generation device 311 includes an X-ray tube 311a, a wedge
311b, and a collimator 311c.
[0130] The X-ray tube 311a generates X-ray. For example, the X-ray
tube 311a is a vacuum tube configured to generate X-ray with high
voltage supplied from a high-voltage generation device not
illustrated. The X-ray tube 311a generates X-ray spreading at a fan
angle and a cone angle.
[0131] The wedge 311b is an X-ray filter for adjusting the amount
of X-ray emitted from the X-ray tube 311a. Specifically, the wedge
311b is a filter configured to transmit and decay the X-ray emitted
from the X-ray tube 311a so that the X-ray emitted from the X-ray
tube 311a to the subject S has pre-defined distribution. For
example, the wedge 311b is a filter fabricated from aluminum to
have a pre-defined target angle and a pre-defined thickness. The
wedge 311b is called a wedge filter or a bow-tie filter.
[0132] The collimator 311c is a slit for narrowing the range of
irradiation with X-ray the amount of which is adjusted through the
wedge 311b under control of the X-ray irradiation control circuitry
312 described later.
[0133] The X-ray irradiation control circuitry 312 controls the
X-ray generation device 311 under control of a scanning control
circuitry 333 described later. For example, the X-ray irradiation
control circuitry 312 controls the high-voltage generation device
not illustrated, so as to supply high voltage to the X-ray tube
311a included in the X-ray generation device 311. The X-ray
irradiation control circuitry 312 adjusts the amount of X-ray
incident on the subject S by adjusting a tube voltage and a tube
current supplied to the X-ray tube 311a. The X-ray irradiation
control circuitry 312 performs switching of the wedge 311b included
in the X-ray generation device 311. The X-ray irradiation control
circuitry 312 adjusts the range of irradiation (the fan angle and
the cone angle) with X-ray by adjusting the opening degree of the
collimator 311c included in the X-ray generation device 311.
[0134] The detector 313 detects X-ray generated from the X-ray tube
311a. For example, the detector 313 is a two-dimensional array
detector (plane detector) configured to detect X-ray having
transmitted through the subject S, in which a plurality of
detection element lines each including arrangement of X-ray
detection elements corresponding to a plurality of channels are
arrayed in a body-axis direction (Z-axis direction illustrated in
FIG. 1) of the subject S. Specifically, the detector 313 in the
present embodiment includes X-ray detection elements arrayed as a
large number of lines, for example, 320 lines in the body-axis
direction of the subject S, and is capable of detecting X-ray
having transmitted through a wide range of the subject S such as a
range including the chest and the heart of the subject S.
[0135] The rotational frame 314 is formed in a circular ring,
supporting the X-ray generation device 311 and the detector 313
such that the X-ray generation device 311 and the detector 313 are
opposite to each other with respect to the subject S.
[0136] The gantry drive circuitry 315 rotates the rotational frame
314 under control of the scanning control circuitry 333 described
later, thereby rotating the X-ray generation device 311 and the
detector 313 on a circular orbit around the subject S at a
center.
[0137] The data collection circuitry 316 collects, under control of
the scanning control circuitry 333 described later, projection data
from detection data of X-ray detected by the detector 313. The data
collection circuitry 316 is also called a data acquisition system
(DAS). For example, the data collection circuitry 316 generates
projection data by performing, for example, amplification
processing, A/D conversion processing, and inter-channel
sensitivity correction processing on X-ray intensity distribution
data detected by the detector 313, and transmit the generated
projection data to the console 330 described later. The
inter-channel sensitivity correction processing may be performed by
a preprocessing circuitry 334 described later.
[0138] The couch 320 is an apparatus on which the subject S is
placed, and includes a couchtop 321 on which the subject S is
actually placed, and a couch drive device 322 as illustrated in
FIG. 1. The couch drive device 322 moves the couchtop 321 in the
Z-axis direction to move the subject S into the rotational frame
314.
[0139] The gantry 310 executes, for example, helical scanning that
helically scans the subject S by rotating the rotational frame 314
while continuously moving the couchtop 321. Alternatively, the
gantry 310 executes conventional scanning that scans the subject S
on a circular orbit by rotating the rotational frame 314 while the
position of the subject S is fixed after moving the couchtop 321.
Alternatively, the gantry 310 executes a step and shoot scheme that
performs the conventional scanning at a plurality of image pickup
positions while moving the position of the couchtop 321 at a
constant interval.
[0140] The console 330 is an apparatus configured to receive an
operation of the X-ray CT apparatus 300 by the operator and
reconstruct CT image data by using projection data collected by the
gantry 310. As illustrated in FIG. 1, the console 330 includes an
input circuitry 331, a display 332, the scanning control circuitry
333, the preprocessing circuitry 334, a storage circuitry 335, an
image reconstruction circuitry 336, and a processing circuitry
337.
[0141] The input circuitry 331 includes a mouse, a keyboard, a
track ball, a switch, a button, and a joystick, which are used by
the operator of the X-ray CT apparatus 300 to input various
instructions and various settings, and forwards, to the processing
circuitry 337, information on instructions and settings received
from the operator. For example, the input circuitry 331 receives,
from the operator, for example, condition of image capturing of CT
image data, condition of reconstructing CT image data, and
condition of image processing on CT image data. The input circuitry
331 receives a specifying operation for specifying a site on an
image, or a pre-defined region such as a region of interest.
[0142] The display 332 is a monitor referred to by the operator,
and displays, under control of the processing circuitry 337, a CT
image generated from CT image data to the operator, and a graphical
user interface (GUI) for receiving, for example, various
instructions and various settings from the operator through the
input circuitry 331.
[0143] The scanning control circuitry 333 controls, under control
of the processing circuitry 337, operation of the X-ray irradiation
control circuitry 312, the gantry drive circuitry 315, the data
collection circuitry 316, and the couch drive device 322, thereby
controlling processing of collecting projection data at the gantry
310.
[0144] The preprocessing circuitry 334 generates corrected
projection data by performing, on projection data generated by the
data collection circuitry 316, logarithmic conversion processing,
and correction processing such as offset correction, sensitivity
correction, and beam hardening correction. Specifically, the
preprocessing circuitry 334 generates corrected projection data
from the projection data generated by the data collection circuitry
316, and stores the corrected projection data in the storage
circuitry 335.
[0145] The storage circuitry 335 stores therein various kinds of
data. For example, the storage circuitry 335 stores therein the
projection data generated by the preprocessing circuitry 334, and
CT image data generated by the image reconstruction circuitry 336
described later. The storage circuitry 335 stores therein various
kinds of data generated as a result of processing executed by the
processing circuitry 337 described later.
[0146] The image reconstruction circuitry 336 reconstructs CT image
data by using the projection data stored by the storage circuitry
335. For example, the image reconstruction circuitry 336
reconstructs the CT image data by back projection processing using
a filtered back projection (FBP) method or the like. Alternatively,
for example, the image reconstruction circuitry 336 reconstructs
the CT image data by using an iterative approximation method. The
image reconstruction circuitry 336 generates various CT images by
performing various kinds of image processing on the CT image data.
Then, the image reconstruction circuitry 336 stores the
reconstructed CT image data and the CT images generated by various
kinds of image processing in the storage circuitry 335. The image
reconstruction circuitry 336 is exemplary processing circuitry in
the claims.
[0147] The processing circuitry 337 performs entire control of the
X-ray CT apparatus 300 by controlling operation of the gantry 310,
the couch 320, and the console 330. Specifically, the processing
circuitry 337 controls CT scanning performed at the gantry 310 by
controlling the scanning control circuitry 333. The processing
circuitry 337 controls image reconstruction processing and image
generation processing at the console 330 by controlling the image
reconstruction circuitry 336. The processing circuitry 337 performs
control to display various CT images stored in the storage
circuitry 335 on the display 332.
[0148] With this configuration, in the X-ray CT apparatus 300
according to the present embodiment, the processing circuitry 337
includes an acquisition function 337a, a specification function
337b, and an analysis function 337c. The specification function
337b is exemplary processing circuitry in the claims.
[0149] The acquisition function 337a executes processing same as
that executed by the acquisition function 151 described in the
first embodiment and the modifications above. In the first
embodiment and the modifications described above, the acquisition
function 151 acquires CT image data or projection data from the
X-ray CT apparatus 300 or the medical image storage apparatus 400.
However, the acquisition function 337a according to the present
embodiment acquires the CT image data or the projection data from
the storage circuitry 335.
[0150] The specification function 337b executes processing same as
that executed by the specification function 152 described in the
first embodiment and the modifications above.
[0151] The analysis function 337c executes processing same as that
executed by the analysis function 153 described in the first
embodiment and the modifications above.
[0152] In the present embodiment, the input circuitry 331, the
display 332, and the storage circuitry 335 have functions same as
those of the input circuitry 130, the display 140, and the storage
circuitry 120 described in the first embodiment above,
respectively.
[0153] According to the second embodiment described above,
similarly to the first embodiment, fluid analysis on the blood flow
can be performed at a higher accuracy.
[0154] The embodiments described above describe the example in
which each processing function is achieved by a single processing
circuitry (the processing circuitry 150 or the processing circuitry
337), but the present embodiment is not limited thereto. For
example, this processing circuitry may be configured as a
combination of a plurality of independent processors, and each
processor may achieve each processing function by executing the
corresponding computer program. The processing functions included
in this processing circuitry may be achieved through distribution
or integration to a single or a plurality of processing circuitries
as appropriate.
[0155] "Processor" used in the descriptions of the embodiments
above refers to, for example, a central processing unit (CPU), a
graphics processing unit (GPU), or a circuitry such as an
application specific integrated circuit (ASIC) and a programmable
logic device (for example, a simple programmable logic device
(SPLD), a complex programmable logic device (CPLD), or a field
programmable gate array (FPGA)). A computer program may be directly
incorporated in the circuitry of such a processor instead of being
stored in a storage circuitry. In this case, the processor achieves
a function by reading and executing the computer program
incorporated in the circuitry. Processors according to the present
embodiment are not only configured as single circuitries, but may
be configured as one processor as a combination of a plurality of
independent circuitries to achieve their functions.
[0156] A computer program executed by the processors is
incorporated and provided in, for example, a read-only memory (ROM)
or a storage circuitry in advance. The computer program may be
recorded and provided, as a file installable or executable at these
processors, in a computer-readable storage medium such as a compact
disc (CD)-ROM, a flexible disk (FD), a CD-R (Recordable), or a
digital versatile disc (DVD). The computer program may be stored in
a computer connected with a network such as the Internet, and
provided or distributed by downloading through the network. For
example, the computer program includes a module including each
functional component described later. The computer program is read
from a storage medium such as a ROM and executed by the CPU as
actual hardware so as to load and generate the module on a main
storage apparatus.
[0157] According to at least one of the embodiments described
above, fluid analysis on the blood flow can be performed at a
higher accuracy.
[0158] While certain embodiments have been described, these
embodiments have been presented by way of example only, and are not
intended to limit the scope of the inventions. Indeed, the novel
embodiments described herein may be embodied in a variety of other
forms; furthermore, various omissions, substitutions and changes in
the form of the embodiments described herein may be made without
departing from the spirit of the inventions. The accompanying
claims and their equivalents are intended to cover such forms or
modifications as would fall within the scope and spirit of the
inventions.
* * * * *